Abstract

Robot local path planning in an unknown and changing environment with uncertainties is
one of the most challenging problems in robotics which involves the integration of many
different bodies of knowledge. This makes mobile robotics a challenge worldwide which
for many years has been investigated by researchers.
Therefore in this thesis, a new fuzzy logic control system is developed for reactive
navigation of a behavior-based mobile robot. The motion of a Pioneer 3TM mobile robot
was simulated to show the algorithm performance. The robot perceives its environment
through an array of eight sonar range finders and self positioning-localization sensors.
The robot environment consists of walls and dead end traps from any size and shape, as
well as other stationary obstacles and it is assumed to be fully unknown. Robot behaviors consist of obstacle avoidance, target seeking, speed control, barrier
following and local minimum avoidance. While the fuzzy logic body of the algorithm
performs the main tasks of obstacle avoidance, target seeking, and speed adjustment, an
actual-virtual target switch strategy integrated with the fuzzy logic algorithm enables the
robot to show wall following behavior when needed. This combinational approach which
uses a new kind of target shift, significantly results in resolving the problem of multiple
minimum in local navigation which is an advantage beyond the pure fuzzy logic
approach and the common virtual target switch techniques.
In this work, multiple traps may have any type of shape or arrangement from barriers
forming simple corners and U-shape dead ends to loops, maze, snail shape, and many
others. Under the control of the algorithm, the mobile robot makes logical trajectories
toward the target, finds best ways out of dead ends, avoids any types of obstacles, and
adjusts its speed efficiently for better obstacle avoidance and according to power
considerations and actual limits.
From TRAINER Software and Colbert Program which were used in the simulation work,
the system managed to solve all the problems in sample environments and the results
were compared with results from other related methods to show the effectiveness and
robustness of the proposed approach.